Using big data methods to understand Alzheimer’s disease

Samuel L. Warren, Ahmed A. Moustafa, Hany Alashwal

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

1 Citation (Scopus)


In the next 30 years, Alzheimer’s disease cases are predicted to drastically increase. Consequently, there is a critical need for research that can counteract the increasing number of Alzheimer’s disease patients. However, current methods of Alzheimer’s disease research have significant limitations. For example, Alzheimer’s disease research is often restricted by resource, temporal, and recruitment barriers (e.g., participant dropout). Unlike standard research, big data analysis is excellent at investigating complex long-term phenomena such as Alzheimer’s disease. Big data methods can also overcome many of the limitations that restrict Alzheimer’s disease research. Accordingly, researchers are turning to big data methods to study Alzheimer’s disease. In this chapter, we outline the applications of big data to Alzheimer’s disease research as well as common methods used to collect and analyze big data. We also explore how big data research could be used to treat, diagnose, and understand Alzheimer’s disease. Accordingly, we aim to provide a general understanding of big data methods in Alzheimer’s disease research and highlight the advantages of big data analysis over standard dementia research.

Original languageEnglish
Title of host publicationAlzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy
PublisherAcademic Press
Number of pages25
ISBN (Electronic)9780128213346
Publication statusPublished - 2021
Externally publishedYes


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